PortsFish.Agency | Trade Intelligence & Data Lab
Climate Risk & Sustainability Intelligence Layer

Climate & Fishing Impact Intelligence is a structured analytical framework designed to assess how climate variability, oceanographic shifts, environmental regulation, and ecosystem stress directly impact seafood supply stability, trade corridors, price volatility, and long-term capital exposure.

In modern seafood trade, climate risk is no longer environmental — it is financial.

PortsFish integrates climate intelligence into trade decision-making.

Strategic Role Within PortsFish

This module connects:

Supply Stability
Price Forecasting
Regulatory Risk
ESG Capital Flows
Trade Finance Structuring
Long-Term Infrastructure Investment

It transforms environmental uncertainty into structured commercial foresight.

1️⃣ Oceanographic & Climate Monitoring Layer

We track and model:

Sea surface temperature anomalies
El Niño / La Niña cycles
Ocean acidification trends
Marine heatwaves
Storm frequency & intensity
Coral bleaching events
Hypoxia zones (oxygen depletion)

These variables directly influence:

Migration patterns
Catch volume volatility
Aquaculture mortality rates
Seasonal production shifts

2️⃣ Wild Catch & Aquaculture Vulnerability Index

We assess:

Species-specific climate sensitivity
Geographic concentration risk
Feed input dependency (for aquaculture)
Freshwater stress exposure
Disease outbreak probability

Output:

Fishing Impact Vulnerability Score (FIVS)
Scaled 0–100.

Higher score = higher climate exposure risk.

3️⃣ Regulatory Climate Pressure Monitoring

Climate impacts regulatory behavior.

We monitor:

Fishing quota reductions
Marine protected area expansions
Carbon footprint reporting mandates
Sustainable sourcing requirements
Anti-IUU enforcement tightening
Import bans linked to environmental compliance

This feeds directly into:

Regulatory Pressure Score (RPS)
Market Access Risk Index

4️⃣ Production Disruption Forecasting

Using climate models and production data, we simulate:

6-month production variability
Seasonal disruption probabilities
Long-term structural yield decline
Aquaculture yield compression under warming scenarios

This integrates with:

Supply Shock Indicator (SSI)
Price Forecasting Engine

5️⃣ Climate-Driven Price Elasticity Modeling

Climate shocks affect pricing via:

Sudden supply compression
Regulatory restrictions
Insurance cost escalation
Fuel price spikes (storm-related logistics disruption)

PortsFish models:

Climate-Adjusted Price Impact Coefficient (CAPIC)
Volatility amplification multiplier
Margin compression probability under climate scenarios

6️⃣ ESG & Capital Exposure Layer

Institutional investors increasingly require:

Climate risk disclosure
Supply chain traceability
Carbon reporting
Sustainable sourcing verification

PortsFish provides:

ESG Compliance Readiness Score
Climate Exposure Transparency Index
Certification adoption growth metrics
Climate-adjusted capital risk rating

This supports:

Green bond structuring
ESG trade finance products
Sustainability-linked credit facilities

7️⃣ Corridor-Level Climate Risk Mapping

We evaluate:

Climate vulnerability by fishing region
Port infrastructure climate exposure
Cold chain resilience index
Storm disruption probability
Insurance premium escalation risk

This produces:

Climate-Adjusted Corridor Attractiveness Score (CACAS)

Used by:

Exporters
Banks
Maritime insurers
Infrastructure investors

8️⃣ Long-Term Structural Risk Modeling

We analyze:

Biomass decline trends
Species migration shifts
Regional fishery collapse probability
Aquaculture expansion feasibility zones
Alternative protein substitution risk

This allows early repositioning of trade corridors.

9️⃣ Scenario Simulation Lab

Users can simulate:

2°C ocean warming impact
Major El Niño event
Quota tightening scenario
Marine protected area expansion
Extreme storm season
Disease outbreak in aquaculture

System recalculates:

Supply Stability Score
Price Forecast
Regulatory Risk
Margin compression
Trade Finance structuring adjustments

🔟 Strategic Outcome

Climate & Fishing Impact Intelligence enables:

• Anticipation of supply compression
• Climate-adjusted pricing strategy
• Regulatory preparedness
• ESG-aligned capital positioning
• Insurance optimization
• Corridor diversification

It transforms environmental volatility into structured strategic advantage.

Institutional Positioning Statement

PortsFish integrates climate intelligence directly into seafood trade infrastructure.

This is not environmental reporting.

It is climate-adjusted trade strategy.

Technical Annex: Fishing Impact Vulnerability Score (FIVS)

PortsFish Trade Intelligence & Data Lab

1. Purpose

The Fishing Impact Vulnerability Score (FIVS) quantifies the climate-and-ecosystem exposure of seafood supply (wild catch and aquaculture) by species, origin geography, and production system.

It is designed to support:

Supply–Demand Analytics
Price Index & Forecasting
Cross-Border Risk Management
ESG underwriting and disclosure
Trade finance covenanting (LC/insurance triggers)
Corridor diversification strategy

Output: A normalized 0–100 score

0–30 = Low vulnerability
31–60 = Moderate vulnerability
61–80 = High vulnerability
81–100 = Critical vulnerability

2. Scope and Unit of Analysis

FIVS is computed for a defined (Species × Origin Region × Production Mode) tuple, where:

Species = commercial species or species group (e.g., shrimp L. vannamei, tuna skipjack)
Origin region = FAO fishing area / EEZ / coastal production zone (or aquaculture region)
Production mode = Wild Catch / Aquaculture (pond, cage, RAS, etc.)

3. Model Architecture (High-Level)

FIVS is a weighted composite of five risk pillars:

Ocean–Climate Hazard Exposure (H)
Biological Sensitivity (S)
Origin Concentration & Mobility Risk (C)
Operational & Infrastructure Fragility (O)
Governance & Adaptation Capacity (G) (inverse factor)

Core formula

$\text{FIVS} = 100 \times \Bigg( w_H \cdot H + w_S \cdot S + w_C \cdot C + w_O \cdot O + w_G \cdot (1-G) \Bigg)$ FIVS=100×(wH⋅H+wS⋅S+wC⋅C+wO⋅O+wG⋅(1−G))

Where each component is normalized to [0,1] and weights sum to 1.

Recommended baseline weights (seafood trade use-case):

$w_H = 0.30$ wH=0.30
$w_S = 0.20$ wS=0.20
$w_C = 0.15$ wC=0.15
$w_O = 0.20$ wO=0.20
$w_G = 0.15$ wG=0.15

(Weights may be calibrated by species class or by investor/bank risk appetite.)

4. Component Definitions (Normalized Sub-Indices)

4.1 Ocean–Climate Hazard Exposure (H) ∈ [0,1]

Measures the intensity and frequency of climate/ocean hazards affecting the origin region. $H = \alpha_1 \cdot \text{SSTA} + \alpha_2 \cdot \text{MHW} + \alpha_3 \cdot \text{ACID} + \alpha_4 \cdot \text{HYPOX} + \alpha_5 \cdot \text{STORM}$ H=α1⋅SSTA+α2⋅MHW+α3⋅ACID+α4⋅HYPOX+α5⋅STORM

Where:

SSTA = Sea Surface Temperature Anomaly index
MHW = Marine Heatwave frequency/severity index
ACID = Ocean acidification trend index (proxy)
HYPOX = Hypoxia/low-oxygen event index
STORM = storm/cyclone disruption index

Weights $\alpha_i$ αi sum to 1. Recommended:

$\alpha_1=0.25,\ \alpha_2=0.25,\ \alpha_3=0.15,\ \alpha_4=0.15,\ \alpha_5=0.20$ α1=0.25, α2=0.25, α3=0.15, α4=0.15, α5=0.20

Normalization: each hazard indicator is scaled using a rolling historical distribution for the region (e.g., min–max with winsorization or percentile rank mapped to [0,1]).

4.2 Biological Sensitivity (S) ∈ [0,1]

Measures species susceptibility to climate stressors and ecological change. $S = \beta_1 \cdot TOL + \beta_2 \cdot REPRO + \beta_3 \cdot DISEASE + \beta_4 \cdot HABITAT$ S=β1⋅TOL+β2⋅REPRO+β3⋅DISEASE+β4⋅HABITAT

Where:

TOL = thermal tolerance risk (inverse of tolerance range)
REPRO = reproductive fragility (slow growth / late maturity risk)
DISEASE = disease susceptibility (especially aquaculture)
HABITAT = dependence on vulnerable habitats (reefs, mangroves, nursery grounds)

Recommended weights:

$\beta_1=0.35,\ \beta_2=0.20,\ \beta_3=0.25,\ \beta_4=0.20$ β1=0.35, β2=0.20, β3=0.25, β4=0.20

Note: For aquaculture species, DISEASE and HABITAT may carry higher weights.

4.3 Origin Concentration & Mobility Risk (C) ∈ [0,1]

Captures exposure arising from geographic concentration and limited substitution. $C = \gamma_1 \cdot CONC + \gamma_2 \cdot SUBST + \gamma_3 \cdot MIG$ C=γ1⋅CONC+γ2⋅SUBST+γ3⋅MIG

Where:

CONC = origin concentration (Herfindahl-Hirschman Index mapped to [0,1])
SUBST = substitutability deficit (low availability of alternative origins/species)
MIG = migration volatility risk (wild catch only; set MIG=0 for aquaculture)

Recommended weights:

$\gamma_1=0.50,\ \gamma_2=0.30,\ \gamma_3=0.20$ γ1=0.50, γ2=0.30, γ3=0.20

4.4 Operational & Infrastructure Fragility (O) ∈ [0,1]

Measures how operational systems amplify climate shock impacts. $O = \delta_1 \cdot CC + \delta_2 \cdot PORT + \delta_3 \cdot ENERGY + \delta_4 \cdot WATER + \delta_5 \cdot INS$ O=δ1⋅CC+δ2⋅PORT+δ3⋅ENERGY+δ4⋅WATER+δ5⋅INS

Where:

CC = cold chain fragility index (temperature control reliability, redundancy)
PORT = port/logistics disruption index (congestion, storm exposure, clearance delays)
ENERGY = power reliability risk (critical for cold storage/processing)
WATER = freshwater stress risk (aquaculture-heavy regions)
INS = insurance cost escalation proxy (or claim frequency proxy)

Recommended weights:

$\delta_1=0.25,\ \delta_2=0.25,\ \delta_3=0.20,\ \delta_4=0.15,\ \delta_5=0.15$ δ1=0.25, δ2=0.25, δ3=0.20, δ4=0.15, δ5=0.15

4.5 Governance & Adaptation Capacity (G) ∈ [0,1]

Represents mitigants: institutional capacity to manage fisheries sustainably and adapt. $G = \theta_1 \cdot MGMT + \theta_2 \cdot ENF + \theta_3 \cdot DATA + \theta_4 \cdot INFRA + \theta_5 \cdot CERT$ G=θ1⋅MGMT+θ2⋅ENF+θ3⋅DATA+θ4⋅INFRA+θ5⋅CERT

Where:

MGMT = fisheries management strength (quota design, stock assessment rigor)
ENF = enforcement capacity (IUU control effectiveness)
DATA = data transparency (monitoring, traceability readiness)
INFRA = adaptive infrastructure investment (resilience measures)
CERT = certification & compliance penetration (MSC/ASC adoption proxy)

Recommended weights:

$\theta_1=0.25,\ \theta_2=0.25,\ \theta_3=0.15,\ \theta_4=0.20,\ \theta_5=0.15$ θ1=0.25, θ2=0.25, θ3=0.15, θ4=0.20, θ5=0.15

Because higher governance reduces vulnerability, the main formula uses (1 − G).

5. Production Mode Adjustments

5.1 Wild Catch Modifier

Wild catch is highly exposed to migration and ecosystem shifts: $\text{FIVS}_{wild} = \text{FIVS} \times (1 + \lambda_{wild}\cdot MIG\_AMP)$ FIVSwild=FIVS×(1+λwild⋅MIG_AMP)

Where $MIG\_AMP \in [0,1]$ MIG_AMP∈[0,1] captures species migration volatility intensity.
Recommended $\lambda_{wild}=0.10$ λwild=0.10.

5.2 Aquaculture Modifier

Aquaculture is sensitive to disease + water + feed dependency: $\text{FIVS}_{aqua} = \text{FIVS} \times (1 + \lambda_{aqua}\cdot DISEASE\_AMP + \mu\cdot WATER)$ FIVSaqua=FIVS×(1+λaqua⋅DISEASE_AMP+μ⋅WATER)

Recommended $\lambda_{aqua}=0.10,\ \mu=0.05$ λaqua=0.10, μ=0.05, capped so final remains ≤100 via winsorization.

6. Calibration & Normalization Rules

6.1 Normalization

All indicators must be mapped into [0,1] using one of:

Percentile rank normalization (preferred for robustness)
Winsorized min–max scaling (p5–p95 range)

6.2 Data Confidence Score (optional overlay)

Each FIVS value may carry a Data Confidence Level (DCL): $DCL = \min(1,\ \text{coverage} \times \text{recency} \times \text{source\_quality})$ DCL=min(1, coverage×recency×source_quality)

Reported alongside FIVS for investor-grade transparency.

7. Output Reporting Format

For each tuple (species × origin × mode), the report provides:

FIVS score (0–100)
Pillar breakdown: H, S, C, O, G
Top drivers (ranked)
Confidence band (High/Med/Low)
Recommended actions:
- corridor diversification
- insurance / LC confirmation triggers
- resilience investments (cold chain / port)
- certification and governance upgrades